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A review about COVID-19 in the MENA region: environmental concerns and machine learning applications

Meskher H 1 , Belhaouari SB 2 , Thakur AK 3 , Sathyamurthy R 4 , Singh P 5 , Khelfaoui I 6 Show all authors , Saidur R 7

Affiliations 

  • 1 Division of Process Engineering, College of Applied Science, Kasdi-Merbah University, 30000, Ouargla, Algeria
  • 2 Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Education City, Qatar Foundation, P.O. Box 34110, Doha, Qatar
  • 3 Department of Mechanical Engineering, KPR Institute of Engineering and Technology, Arasur, Coimbatore, Tamil Nadu, 641407, India
  • 4 Department of Mechanical Engineering, King Fahd University of Petroleum and Minerals, Dammam, Saudi Arabia. raviannauniv23@gmail.com
  • 5 Institute of Engineering and Technology, Department of Mechanical Engineering, GLA University Mathura, Mathura, Uttar Pradesh, 281406, India
  • 6 School of Insurance and Economics, University of International Business and Economics, Beijing, China
  • 7 Research Centre for Nano-Materials and Energy Technology (RCNMET), School of Engineering and Technology, Sunway University, No. 5, Jalan Universiti, Bandar Sunway, 47500, Petaling Jaya, Malaysia
Environ Sci Pollut Res Int, 2022 Nov;29(55):82709-82728.
PMID: 36223015 DOI: 10.1007/s11356-022-23392-z

Abstract

Coronavirus disease 2019 (COVID-19) has delayed global economic growth, which has affected the economic life globally. On the one hand, numerous elements in the environment impact the transmission of this new coronavirus. Every country in the Middle East and North Africa (MENA) area has a different population density, air quality and contaminants, and water- and land-related conditions, all of which influence coronavirus transmission. The World Health Organization (WHO) has advocated fast evaluations to guide policymakers with timely evidence to respond to the situation. This review makes four unique contributions. One, many data about the transmission of the new coronavirus in various sorts of settings to provide clear answers to the current dispute over the virus's transmission were reviewed. Two, highlight the most significant application of machine learning to forecast and diagnose severe acute respiratory syndrome coronavirus (SARS-CoV-2). Three, our insights provide timely and accurate information along with compelling suggestions and methodical directions for investigators. Four, the present study provides decision-makers and community leaders with information on the effectiveness of environmental controls for COVID-19 dissemination.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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